Department of Statistics
http://ugspace.ug.edu.gh:8080/handle/123456789/34524
2024-03-28T14:24:24ZValidation of Malaria Predictive Model with Missing Covariates
http://ugspace.ug.edu.gh:8080/handle/123456789/34691
Validation of Malaria Predictive Model with Missing Covariates
Duah, D.
This study develops a statistical methodology for validating a predictive clinical malaria model when data has missing values in predictor variables. Using the logistic regression framework, multiple imputation techniques for missing values and a penalized likelihood approach to avoid overfitting of the data were adopted. Models with different functional forms were built using known predictors (age, sickle cell, blood group, parasite density, and mosquito bed net use) and some malaria antibody specific antigens and FCGR3B polymorphism. Models were assessed through visualization and differences between the Area Under the Receiver Operating Characteristic Curve (AUROC) and Brier Score (BS) estimated by suitable internal cross-validation designs. The contributions of this research are in three folds: (i) addresses the statistical question on how to build and validate a risk prediction model in the presence of missing explanatory variables (ii) improves the general statistical approach for malaria epidemiology (iii) identifies potential malaria antibodies and FCGR3B polymorphism which should be the research focus in the search for potential malaria vaccine candidate
Departmental seminar
2016-09-29T00:00:00ZOn the Estimation of Conditional Tail Index and Extreme Quantiles under Random Censoring
http://ugspace.ug.edu.gh:8080/handle/123456789/34690
On the Estimation of Conditional Tail Index and Extreme Quantiles under Random Censoring
Minkah, R.
In the area of Statistics of Extremes, the main assumption on any set of univariate data is to regard them as a complete sample of independent and identically distributed observations from an unknown distribution function, F. However, in many real life applications such as survival analysis, observations are usually subject to random censoring and may be influenced by an underlying covariate information. In such case, the classical extreme value theory needs some adjustment to take into account the presence of censoring and covariates. In this presentation, we propose estimators of the conditional tail index and conditional extreme quantiles for heavy-tailed distributions in the presence of random censoring and covariate information. We compare the proposed estimators with the existing estimators in the literature in a large scale simulation study. The results show improvement in bias and median absolute deviation over the existing estimators of the conditional tail index and conditional extreme quantiles.
Departmental Seminar
2016-09-29T00:00:00Z“Maximum Penalized Likelihood Approach for Current Status Data with Informative Censoring Based on Frailties”
http://ugspace.ug.edu.gh:8080/handle/123456789/34620
“Maximum Penalized Likelihood Approach for Current Status Data with Informative Censoring Based on Frailties”
Faisal, A.; Mettle, F.O.
Current status data arise in many fields including epidemiological surveys and animal carcinogenicity experiments. By current status data we mean each subject in a study is observed only once at a certain time to ascertain whether or not the event of interest has occurred. This means that for current status data, the event (failure) time is not exactly observed but only known to be smaller or greater than the observation (censoring) time. The standard assumption in survival data analysis is that the observation time is non-informative, that is, the observation time is unrelated to the failure time. However, this assumption may not be valid in some circumstances, especially if the subject is lost-to-follow-up before the study ends. The examination time for such a subject can potentially be linked to the failure time and hence the examination time will be said to be informatively censored. To avoid severely misleading inferences that can result from failing to account for informative censoring, shared frailty models have been proposed in literature to account for informative censoring with current status data. The most commonly used estimation procedure is the Expectation-Maximization (EM) algorithm, but this approach yield discrete estimation of the distribution and possibly a negative hazard estimate. Thus the EM algorithm might not only be able to provide an accurate trend of how the baseline hazard estimates are changing over time, but also affect the accuracy of the estimated parameters. The goal of this thesis therefore, is to develop Maximum Penalized Likelihood (MPL) methods for current status data with informative censoring based on gamma-shared frailty assumption. We intend to show how the MPL procedure can be employed to simultaneously estimate the regression parameters and the smooth baseline continuous hazard functions for Proportional Hazards Model, Semiparametric transformation Model and Clustered data under the proportional hazards model. The variances and the asymptotic properties of these MPL estimators will be studied. Simulations studies will also be conducted to ascertain the performance of our proposed MPL methods. Comparisons will also be made with some of the existing EM methods. For illustrative purposes, we will apply our methods to the data set on rodent tumorigenicity experiment example provided by National Toxicology Program (1998)
Seminar
2017-02-09T00:00:00ZImplementation of Fourier Transform in Image Processing
http://ugspace.ug.edu.gh:8080/handle/123456789/34400
Implementation of Fourier Transform in Image Processing
Ansah-Narh, T.
For a realistic radio interferometer for flat-sky approximation, the basic observation is a set of complex visibilities, which is defined in 2D Fourier Transform (FT) as Radio Measurement Interferometer Measurement Equation (RIME). The Discrete Fourier Transform (DFT) is a specific form of Fourier analysis that is widely employed in signal processing and related fields to analyze frequencies contained in a sample signal to solve partial differential equations and to perform other operations such as convolutions. The presentation will focus on the implementation of the algorithm of Fast Fourier Transform (FFT) by converting a 2D image to the frequency domain and back to the image domain (Inverse FFT). The technique will then be related to RIME.
Seminar
2017-02-09T00:00:00Z